The Spam Detection Website is a machine learning-based application that identifies and filters out spam emails. It helps users manage their email inboxes efficiently by separating legitimate emails from unwanted spam.
Email Classification: The website classifies incoming emails as either “Spam” or “Not Spam.” User-Friendly Interface: Users can easily upload email content or paste text for classification. Model Accuracy: The underlying machine learning model has been trained on a large dataset to achieve high accuracy.
Clone this repository to your local machine: git clone https://github.com/govindsingh3477/SMS-DETECTOR.git
Install the required dependencies:
(pip install -r requirements.txt)
Run the web application:
( streamlit run .\app.py)
Access the website at http://localhost:8501 in your web browser. Paste an email or upload a text file to check if it’s spam or not.
We used the SMS Spam Collection Dataset from Kaggle(https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset). The dataset contains labeled emails (spam/not spam) for training and evaluation.
Algorithm: Naive Bayes Preprocessing: Tokenization, stop-word removal, and TF-IDF vectorization Contributing Contributions are welcome! If you find any issues or want to enhance the model, feel free to submit a pull request.